Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling

Floodplain forests serve a critical function in the global carbon cycle because floodplains constitute an important carbon sink compared with other terrestrial ecosystems. Forests on dynamic floodplain landscapes, such as those created by river meandering processes, are characterized by uneven-aged trees and exhibit high spatial variability, reflecting the influence of interacting fluvial, hydrological, and ecological processes. Detailed and accurate mapping of aboveground biomass (AGB) on floodplain landscapes characterized by uneven-aged forests is critical for improving estimates of floodplain-forest carbon pools, which is useful for greenhouse gas (GHG) life cycle assessment. It would also help improve our process understanding of biomorphodynamics of river-floodplain systems, as well as planning and monitoring of conservation, restoration, and management of riverine ecosystems. Using stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), and Cubist, we remotely estimate AGB of a bottomland hardwood forest on a meander bend of a dynamic lowland river. As predictors, we use 30-m and 10-m multispectral image bands (Landsat 7 ETM+ and SPOT 5, respectively) and ancillary data. Our findings show that SGB and MARS significantly outperform Cubist, which is used for U.S. national-scale forest biomass mapping. Across all data-experiments and algorithms, at 10-m spatial resolution, SGB yields the 2 best estimates (RMSE = 22.49 tonnes/ha; coefficient of determination (R ) = 0.96) when geomorphometric data are also included. On the other hand, at 30-m spatial resolution, MARS yields the best estimates (RMSE = 29.2 tonnes/ha; R2 = 0.94) when image-derived data are also included. By enabling more accurate AGB mapping of floodplains characterized by uneven-aged forests, SGB and MARS provide an avenue for improving operational estimates of AGB and carbon at local, regional/continental, and global scales. © 2014 Elsevier B.V. All rights reserved.